Towards Uniformly Efficient Trend Estimation Under Weak/Strong Correlation and Non‐stationary Volatility

In this paper, we consider the deterministic trend model where the error process is allowed to be weakly or strongly correlated and subject to non‐stationary volatility. Extant estimators of the trend coefficient are analysed. We find that under heteroskedasticity, the Cochrane–Orcutt‐type estimator (with some initial condition) could be less efficient than Ordinary Least Squares (OLS) when the process is highly persistent, whereas it is asymptotically equivalent to OLS when the process is less persistent. An efficient non‐parametrically weighted Cochrane–Orcutt‐type estimator is then proposed. The efficiency is uniform over weak or strong serial correlation and non‐stationary volatility of unknown form. The feasible estimator relies on non‐parametric estimation of the volatility function, and the asymptotic theory is provided. We use the data‐dependent smoothing bandwidth that can automatically adjust for the strength of non‐stationarity in volatilities. The implementation does not require pretesting persistence of the process or specification of non‐stationary volatility. Finite‐sample evaluation via simulations and an empirical application demonstrates the good performance of proposed estimators.     

LOG-PERIODOGRAM ESTIMATION OF LONG MEMORY VOLATILITY DEPENDENCIES WITH CONDITIONALLY HEAVY TAILED RETURNS

Many recent papers have used semiparametric methods, especially the log-periodogram regression, to detect and estimate long memory in the volatility of asset returns. In these papers, the volatility is proxied by measures such as squared, log-squared, and absolute returns. While the evidence for the existence of long memory is strong using any of these measures, the actual long memory parameter estimates can be sensitive to which measure is used. In Monte-Carlo simulations, I find that if the data is conditionally leptokurtic, the log-periodogram regression estimator using squared returns has a large downward bias, which is avoided by using other volatility measures. In United States stock return data, I find that squared returns give much lower estimates of the long memory parameter than the alternative volatility measures, which is consistent with the simulation results. I conclude that researchers should avoid using the squared returns in the semiparametric estimation of long memory volatility dependencies.     

Refined Inference on Long Memory in Realized Volatility

There is an emerging consensus in empirical finance that realized volatility series typically display long range dependence with a memory parameter (d) around 0.4 (Andersen et al., 2001; Martens et al., 2004). The present article provides some illustrative analysis of how long memory may arise from the accumulative process underlying realized volatility. The article also uses results in Lieberman and Phillips (2004, 2005) to refine statistical inference about d by higher order theory. Standard asymptotic theory has an O(n^-1/2) error rate for error rejection probabilities, and the theory used here refines the approximation to an error rate of o(n^-1/2). The new formula is independent of unknown parameters, is simple to calculate and user-friendly. The method is applied to test whether the reported long memory parameter estimates of Andersen et al. (2001) and Martens et al. (2004) differ significantly from the lower boundary (d = 0.5) of nonstationary long memory, and generally confirms earlier findings.     

电力系统同步网的建设与思考

数字同步网已经成为了通信网营运的重要支柱之一。针对电力系统通信业务面向市场开放的需求 ,阐述了建设电力系统同步网的重要性 ;研究和分析了同步网中时钟配置、链路选择和同步方式、网络管理等技术问题 ,并对电力系统中同步网的发展提出了一些建议。     

HEAVY-TAILED-DISTRIBUTED THRESHOLD STOCHASTIC VOLATILITY MODELS IN FINANCIAL TIME SERIES

To capture mean and variance asymmetries and time‐varying volatility in financial time series, we generalize the threshold stochastic volatility (THSV) model and incorporate a heavy‐tailed error distribution. Unlike existing stochastic volatility models, this model simultaneously accounts for uncertainty in the unobserved threshold value and in the time‐delay parameter. Self‐exciting and exogenous threshold variables are considered to investigate the impact of a number of market news variables on volatility changes. Adopting a Bayesian approach, we use Markov chain Monte Carlo methods to estimate all unknown parameters and latent variables. A simulation experiment demonstrates good estimation performance for reasonable sample sizes. In a study of two international financial market indices, we consider two variants of the generalized THSV model, with US market news as the threshold variable. Finally, we compare models using Bayesian forecasting in a value‐at‐risk (VaR) study. The results show that our proposed model can generate more accurate VaR forecasts than can standard models.     

上海股票市场波动实证性研究:2001--从交易方式角度透视

我国股票市场上的交易方式有两种,即集合竞价和连续竞价,交易方式的差异会对股价的波动性产生影响,而市场的波动性对股票市场而言是双刃剑,因此从交易方式角度探求股市的适度波动成为理论工作的一个重心.本文以上海股票市场为研究对象,针对2001年的全部交易数据进行实证研究.结果表明,集合竞价形成的开盘价格收益率的方差大于连续竞价形成的收盘价格收益率的方差,其原因在于集合竞价与连续竞价相比,其交易过程的透明度差和交易指令具有不可更改性,据此,从交易制度上提出政策建议.     

Race and Population Change: A Longitudinal Look at Cleveland Neighborhoods

This paper investigates the relationship of racial composition to neighborhood population change from 1910 to 1990 in the Cleveland metropolitan area. To better understand the long-term dynamics of urban neighborhood change, we focus our analysis upon the longitudinal relationship of race, socioeconomic status, and life cycle stage to changes in neighborhood population densities. First, we find that the more established neighborhoods of the African-American community have experienced dramatic declines in population since 1950, a pattern that represents a clear change from the earlier part of the twentieth century. Second, population loss is experienced through a variety of mechanisms, including the demolition of dwellings, the increase in housing vacancy, and the decline of household size. Third, much of this population loss should be interpreted within the context of high economic distress, occurring most frequently in older African-American communities. Over time, economic distress appears to be more important than race in and of itself in leading to the loss of neighborhood populations.     

The transition to parenthood in Puerto Rico: Occupational status and the timing of first births

This study examines the relationship between occupational status and fertility timing during a period of rapid development in Puerto Rico. Our fundamental hypothesis is that women with higher status occupations face greater opportunity costs than those with less valued jobs and therefore will be more likely to postpone parenthood until later ages than women without such high costs. We test this hypothesis using event history techniques with data from the 1982 Puerto Rico Fertility and Family Planning Assessment, an island-wide survey of women between the ages of 15 and 49. The analysis examines the effects of occupational status on the timing of first births, and finds strong support for the basic hypothesis, especially regarding the post-ponement of teen births. After the teen years, the effects are less pronounced. Overall, it appears that employment opportunities have played an important role in childbearing decisions in Puerto Rico.An earlier version of this paper was presented at the annual meeting of the Population Association of America, Denver, Colorado, 30 April–2 May 1992.     

Finite Sample Performance in Cointegration Analysis of Nonlinear Time Series with Long Memory

Nonlinear functions of multivariate financial time series can exhibit long memory and fractional cointegration. However, tools for analysing these phenomena have principally been justified under assumptions that are invalid in this setting. Determination of asymptotic theory under more plausible assumptions can be complicated and lengthy. We discuss these issues and present a Monte Carlo study, showing that asymptotic theory should not necessarily be expected to provide a good approximation to finite-sample behavior.     

Conditional Likelihood Estimators for Hidden Markov Models and Stochastic Volatility Models

ABSTRACT.  This paper develops a new contrast process for parametric inference of general hidden Markov models, when the hidden chain has a non-compact state space. This contrast is based on the conditional likelihood approach, often used for ARCH-type models. We prove the strong consistency of the conditional likelihood estimators under appropriate conditions. The method is applied to the Kalman filter (for which this contrast and the exact likelihood lead to asymptotically equivalent estimators) and to the discretely observed stochastic volatility models.